Using computer vision and neural networks for defect detection in manufacturing

dc.contributor.author	Sotnychenko, Viktor
dc.date.accessioned	2025-10-19T12:00:18Z
dc.date.issued	2025
dc.description.abstract	The research explores the application of computer vision and neural networks for automated defect detection in manufacturing processes. Deep learning-based algorithms analyze visual data to identify defects, optimize quality control, and enhance production efficiency [1]. The study aims to reduce human error, increase detection accuracy, and minimize production losses.
dc.identifier.citation	Sotnychenko V. Using computer vision and neural networks for defect detection in manufacturing [Electronic resours] / Viktor Sotnychenko ; research supervisor Igor Yakovenko ; lang. support supervisor Tetyana Sergeyeva // An Innovative Model of Research Projects Aimed at the Integration of Ukraine into the European Scientific Space : book of abstr. an Annual Intern. PhD Conf., April 24, 2025 / National Technical University "Kharkiv Polytechnic Institute". – Electronic text data. – Kharkiv : NTU "KhPI", 2025. – P. 250-252.
dc.identifier.uri	https://repository.kpi.kharkov.ua/handle/KhPI-Press/94191
dc.language.iso	en
dc.publisher	National Technical University "Kharkiv Polytechnic Institute"
dc.subject	computer vision
dc.subject	neural networks
dc.subject	defect detection
dc.subject	deep learning
dc.subject	automation
dc.subject	manufacturing quality control
dc.title	Using computer vision and neural networks for defect detection in manufacturing
dc.type	Article

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